This invention relates generally to imaging systems and more particularly to systems and methods for segmenting an organ in a plurality of images.
Several modalities are used to image a patient's internal anatomy or alternatively the patient's functionality. During clinical diagnosis, the images are obtained to determine how a disease has progressed. For example, the images help distinguish between infected tissues (such as a tumor mass, for example) and healthy tissues within the patient. As another example, the images may help distinguish between differences present within the healthy tissues.
The images are also useful for radiotherapy planning (RT) or alternatively for surgical planning. In the case of RT planning, computed tomography (CT) imaging is generally used because intensity values are a function of radiation dose calculation. A CT image is three dimensional (3D), and more precisely, it is a collection of adjacent transaxial two dimensional (2D) slices. Clinicians, such as radiologists, dosimetrists, and radiotherapists, recombine anatomical elements of 2D slices to form a 3D organ image that includes anatomical data about the patient.
RT planning typically involves tracing outlines of a few critical structures on a large number of images. Manually drawing the outlines on a contiguous set of 2D slices and combining the 2D slices can be time consuming and labor intensive. The time and labor increases significantly with the number of image slices, and the number and size of organs in an anatomical area of interest. The quality of the outlining and the resultant 3D organ image depend on the resolution and contrast of the 2D slices, and on the knowledge and judgment of the clinicians.
Some automated methods for segmenting the organ provide a solution that reduces the time and labor associated with manually segmenting the organ. For example, in one automated method, a region and an enclosing edge of a spinal canal are obtained by tracing a set of images. Once a portion of the region is obtained, pixels surrounding an edge of the portion are examined to determine whether the pixels should be included within the portion of the region. However, in automated methods, “leaking out”, described below, occurs, where the organ leaks out of a boundary of the organ. For example, in the method where the pixels surrounding the edge are examined, leaking out occurs when a contour of the spinal canal cannot be identified in an image due to a partial volume averaging effect or alternatively due to an open vertebra.